title: Identifying candidate disease genes using a trace norm constrained bipartite raking model.
abstract: Computational prediction of genes that play roles in human diseases remains an important but challenging task. In this work, we formulate candidate gene prediction as a bipartite ranking problem combining a task-wise ordered observation model with a latent multitask regression function using the matrix-variate Gaussian process (MV-GP). We then use a trace-norm constrained variational inference approach to obtain the bipartite ranking model variables and the parameters of the underlying multitask regression model. We use this model to predict candidate genes from two gene-disease association data sets and show that our model outperforms current state-of-the-art methods. Finally, we demonstrate the practical utility of our method by successfully recovering well characterized gene-disease associations hidden in our training data.
authors.forename: Cheng H
authors.initials: CH
authors.lastname: Lee
authors.forename: Oluwasanmi
authors.initials: O
authors.lastname: Koyejo
authors.forename: Joydeep
authors.initials: J
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics; D001057:Apolipoproteins E / Q000235:genetics; D019540:Area Under Curve; D001249:Asthma / Q000235:genetics; D002097:C-Reactive Protein / Q000235:genetics; D016208:Databases, Factual; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D006801:Humans; D018950:Lipopolysaccharide Receptors / Q000235:genetics; D008297:Male; D008957:Models, Genetic; D016011:Normal Distribution; D011471:Prostatic Neoplasms / Q000235:genetics; D012372:ROC Curve; D042461:Vascular Endothelial Growth Factor A / Q000235:genetics
chemical_list: D001057:Apolipoproteins E; D018950:Lipopolysaccharide Receptors; D042461:Vascular Endothelial Growth Factor A; D002097:C-Reactive Protein